code: def test_default_model():
      df = generate_unstructured_test_data(1000)
      features = ["A", "B", "C", "D", "E"]
      dataset = Dataset(df=df, target="target", features=features)
  
      lofo = LOFOImportance(
          dataset, cv=4, scoring="neg_mean_absolute_error"
      )
      importance_df = lofo.get_importance()
      assert (
          "E" in lofo.fit_params["categorical_feature"]
      ), "Categorical feature is not detected!"
      assert (
          len(features) == importance_df.shape[0]
      ), "Missing importance value for some features!"
  
      df_checkpoint = df.copy()
  
      dataset = Dataset(
          df=df, target="binary_target", features=features
      )
      lofo = LOFOImportance(dataset, cv=4, scoring="roc_auc")
      importance_df = lofo.get_importance()
  
      assert (
          "E" in lofo.fit_params["categorical_feature"]
      ), "Categorical feature is not detected!"
      assert df.equals(
          df_checkpoint
      ), "LOFOImportance mutated the dataframe!"
      assert (
          importance_df["feature"].values[0] == "E"
      ), "Most important feature is different than E!"
  
file_path: lofo-importance/tests/test_lofo_importance.py
imports: from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.linear_model import LogisticRegression
  from lightgbm import LGBMClassifier
  from sklearn.model_selection import KFold
  from lofo import LOFOImportance, Dataset, plot_importance
  from data.test_data import generate_test_data, generate_unstructured_test_data
label_desc: Use image node_id 3 for calling a global function with example usage: test_default_model() without return types